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Fix numpy>=2 compatibility in cdoTimeAverager and frepytoolsTimeAverager #721

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Merged
ilaflott merged 9 commits into from
Feb 4, 2026
Merged

Fix numpy>=2 compatibility in cdoTimeAverager and frepytoolsTimeAverager #721

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b091575
Initial plan
Copilot Feb 4, 2026
2103c9e
Fix numpy>=2 compatibility in cdoTimeAverager
Copilot Feb 4, 2026
dfaae2f
Optimize moveaxis operation in cdoTimeAverager
Copilot Feb 4, 2026
6a5d26e
Remove redundant copy operations in cdoTimeAverager
Copilot Feb 4, 2026
b0a724d
Update pull_request branch filter to '**'
ilaflott Feb 4, 2026
64a9300
Update pull_request trigger branch filter
ilaflott Feb 4, 2026
f30edfb
Remove main branch trigger from build_conda workflow
ilaflott Feb 4, 2026
90f7803
Fix numpy>=2 compatibility in frepytoolsTimeAverager
Copilot Feb 4, 2026
4ea8fa3
Improve comments in frepytoolsTimeAverager
Copilot Feb 4, 2026
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1 change: 0 additions & 1 deletion .github/workflows/build_conda.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@ name: build_conda
on:
pull_request:
branches:
- main

# cancel running jobs if theres a newer push
concurrency:
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2 changes: 1 addition & 1 deletion .github/workflows/create_test_conda_env.yml
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Expand Up @@ -3,7 +3,7 @@ name: create_test_conda_env
on:
pull_request:
branches:
- main

push:
branches:
- main
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12 changes: 9 additions & 3 deletions fre/app/generate_time_averages/cdoTimeAverager.py
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Expand Up @@ -48,9 +48,15 @@ def generate_timavg(self, infile = None, outfile = None):
nc_fin = Dataset(infile, 'r')

time_bnds = nc_fin['time_bnds'][:].copy()
wgts = ( np.moveaxis(time_bnds, 0, -1)[1][:].copy() - \
np.moveaxis(time_bnds, 0, -1)[0][:].copy() )
wgts_sum = sum(wgts)
# Ensure float64 precision for consistent results across numpy versions
# NumPy 2.0 changed type promotion rules (NEP 50), so explicit casting
# is needed to avoid precision differences
time_bnds = np.asarray(time_bnds, dtype=np.float64)
# Transpose once to avoid redundant operations
time_bnds_transposed = np.moveaxis(time_bnds, 0, -1)
wgts = time_bnds_transposed[1] - time_bnds_transposed[0]
# Use numpy.sum for consistent dtype handling across numpy versions
wgts_sum = np.sum(wgts, dtype=np.float64)

fre_logger.debug('wgts_sum = %s', wgts_sum)

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20 changes: 11 additions & 9 deletions fre/app/generate_time_averages/frepytoolsTimeAverager.py
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Expand Up @@ -81,13 +81,16 @@ def generate_timavg(self, infile = None, outfile = None):
fin_dims = nc_fin.dimensions
num_time_bnds = fin_dims['time'].size
if not self.unwgt: #compute sum of weights
wgts = ( numpy.moveaxis( time_bnds, 0, -1 )[1][:].copy() - \
numpy.moveaxis( time_bnds, 0, -1 )[0][:].copy() )
# Cast to float64 for consistent results across numpy versions (NEP 50 type promotion changes)
time_bnds = numpy.asarray(time_bnds, dtype=numpy.float64)
# Transpose once to avoid redundant operations
time_bnds_transposed = numpy.moveaxis(time_bnds, 0, -1)
wgts = time_bnds_transposed[1] - time_bnds_transposed[0]
# Use numpy.ma.sum only if there are actually masked values in time_bnds
if has_masked_time_bnds:
wgts_sum = numpy.ma.sum(wgts)
wgts_sum = numpy.ma.sum(wgts, dtype=numpy.float64)
else:
wgts_sum = sum(wgts)
wgts_sum = numpy.sum(wgts, dtype=numpy.float64)

fre_logger.debug('wgts_sum = %s', wgts_sum)

Expand Down Expand Up @@ -123,8 +126,8 @@ def generate_timavg(self, infile = None, outfile = None):
if has_masked_data:
avgvals[0][lat][lon] = numpy.ma.sum(tim_val_array * wgts) / wgts_sum
else:
avgvals[0][lat][lon] = sum( (tim_val_array[tim] * wgts[tim] )
for tim in range(num_time_bnds) ) / wgts_sum
# Use numpy.sum for consistent dtype handling across numpy versions
avgvals[0][lat][lon] = numpy.sum(tim_val_array * wgts, dtype=numpy.float64) / wgts_sum

del tim_val_array
del lon_val_array
Expand All @@ -139,9 +142,8 @@ def generate_timavg(self, infile = None, outfile = None):
if has_masked_data:
avgvals[0][lat][lon] = numpy.ma.sum(tim_val_array) / num_time_bnds
else:
avgvals[0][lat][lon] = sum( # no time sum needed here, b.c. unweighted, so sum
tim_val_array[tim] for tim in range(num_time_bnds)
) / num_time_bnds
# Use numpy.sum for consistent dtype handling across numpy versions
avgvals[0][lat][lon] = numpy.sum(tim_val_array, dtype=numpy.float64) / num_time_bnds

del tim_val_array
del lon_val_array
Expand Down